Abstract
Distracted driving jeopardizes the safety of the driver and others. Numerous solutions have been proposed to prevent distracted driving, but the number of related accidents has not decreased. Such a deficiency comes from fragile system designs where drivers are detected exploiting sensory features from strictly controlled vehicle-riding actions and unreliable driving events. We propose a system called ADDICT (Accurate Driver Detection exploiting Invariant Characteristics of smarTphone sensors), which identifies the driver utilizing the inconsistency between gyroscope and magnetometer dynamics and the interplay between electromagnetic field emissions and engine startup vibrations. These features are invariantly observable regardless of smartphone positions and vehicle-riding actions. To evaluate the feasibility of ADDICT, we conducted extensive experiments with four participants and three different vehicles by varying vehicle-riding scenarios. Our evaluation results demonstrated that ADDICT identifies the driver’s smartphone with 89.1% average accuracy for all scenarios and >85% under the extreme scenario, at a marginal cost of battery consumption.
Highlights
Driving while distracted (DWD) is a grave threat to the safety of the driver and others
We propose a system called ADDICT (Accurate Driver Detection exploiting Invariant Characteristics of smarTphone sensors) exploiting two invariantly observable sensory features found when entering and starting a vehicle
Seats with an accuracy of 93.3∼90.0%, 90.0∼86.7%, and 90.0∼80.0% for the three respective scenarios. These results clearly demonstrate that ADDICT maintains a high level of accuracy in distinguishing between the driver and passengers, even in extreme settings
Summary
Driving while distracted (DWD) is a grave threat to the safety of the driver and others. Considering the risk of distracted driving to public safety, a number of countries banned smartphone use for all drivers [4]. To further enforce the law, smartphone manufacturers and mobile service providers distributed various smartphone apps to reduce or prevent distracted driving. There still exists an increasing number of distracted-driver-related incidents due mainly to their limitation of users having to manually identify themselves as the driver. Such an approach is highly unreliable because users may be reluctant to restrict their favorite mobile services or forget to do so
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